# An Extension Of Combinatorial Contextuality For Cognitive Protocols

arXiv: https://arxiv.org/abs/2202.08209 · HTML: https://mlsanity.com/paper/2202.08209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Review of The Book of Why: The New Science of Cause and Effect](https://mlsanity.com/paper/2003.11635/) (2020)
- [A probabilistic framework for analysing the compositionality of conceptual combinations](https://mlsanity.com/paper/1305.5753/) (2013)
- [Is there contextuality in behavioral and social systems?](https://mlsanity.com/paper/1504.07422/) (2015)
- [A unifying framework for relaxations of the causal assumptions in Bell's theorem](https://mlsanity.com/paper/1411.4648/) (2014)
- [Snow Queen is Evil and Beautiful: Experimental Evidence for Probabilistic Contextuality in Human Choices](https://mlsanity.com/paper/1711.00418/) (2017)
- [Violation of contextual generalization of the Leggett-Garg inequality for recognition of ambiguous figures](https://mlsanity.com/paper/1401.2897/) (2014)
- [True Contextuality Beats Direct Influences in Human Decision Making](https://mlsanity.com/paper/1807.05684/) (2018)
- [Contextuality in Human Decision Making in the Presence of Direct Influences: A Comment on Basieva et al. (2019)](https://mlsanity.com/paper/1905.12570/) (2019)

## Abstract (arXiv)

This article extends the combinatorial approach to support the determination of contextuality amidst causal influences. Contextuality is an active field of study in Quantum Cognition, in systems relating to mental phenomena, such as concepts in human memory [Aerts et al., 2013]. In the cognitive field of study, a contemporary challenge facing the determination of whether a phenomenon is contextual has been the identification and management of disturbances [Dzhafarov et al., 2016]. Whether or not said disturbances are identified through the modelling approach, constitute causal influences, or are disregardableas as noise is important, as contextuality cannot be adequately determined in the presence of causal influences [Gleason, 1957]. To address this challenge, we first provide a formalisation of necessary elements of the combinatorial approach within the language of canonical9 causal models. Through this formalisation, we extend the combinatorial approach to support a measurement and treatment of disturbance, and offer techniques to separately distinguish noise and causal influences. Thereafter, we develop a protocol through which these elements may be represented within a cognitive experiment. As human cognition seems rife with causal influences, cognitive modellers may apply the extended combinatorial approach to practically determine the contextuality of cognitive phenomena.